This paper demonstrates that our LMM-based approach not only significantly reduces the computational complexity required for sampling based per-title video encoding—by an astounding 13 times—but also maintains the same level of bitrate saving. These findings not only pave the way for more efficient and adaptive video encoding strategies but also highlight the potential of multi-modal models in enhancing multimedia processing tasks. 

Abstract

In the realm of video encoding, achieving the optimal balance between encoding efficiency and computational complexity remains a formidable challenge. This paper introduces a groundbreaking framework that utilizes a Large Multi-modal Model (LMM) to revolutionize the process of per-title video encoding optimization. By harnessing the predictive capabilities of LMMs, our framework estimates the encoding complexity of video content with unprecedented accuracy, enabling the dynamic selection of encoding configurations tailored to each video’s unique characteristics. The proposed framework marks a significant departure from traditional per-title encoding methods, which often rely on expensive and time-consuming sampling in the rate-distortion space. Through a comprehensive set of experiments, we demonstrate that our LMM-based approach not only significantly reduces the computational complexity required for sampling based per-title video encoding—by an astounding 13 times—but also maintains the same level of bitrate saving. These findings not only pave the way for more efficient and adaptive video encoding strategies but also highlight the potential of multi-modal models in enhancing multimedia processing tasks. The implications of this research extend beyond the immediate improvements in encoding efficiency, offering a glimpse into the future of multimedia content distribution and consumption in an increasingly video-centric digital landscape.

Introduction

Adaptive streaming [1] has become the cornerstone of modern video delivery, enabling content providers to offer a seamless viewing experience across a wide range of devices and network conditions. This technology dynamically adjusts video quality during playback, based on the user’s bandwidth and device capabilities, utilizing a predefined set of bitrate quality pairs known as a bitrate ladder. However, the traditional “one-size-fits-all” approach [2-4] to constructing these bitrate ladders often falls short. It fails to account for the unique characteristics of each video, leading to suboptimal use of bandwidth and a compromised viewing experience.

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